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Molecular Dynamics Simulation in Drug Discovery: Opportunities and Challenges

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Innovations and Implementations of Computer Aided Drug Discovery Strategies in Rational Drug Design

Abstract

Drug discovery is the process used to discover new candidate medications. In the past, most drugs were discovered by identification of active-ingredients or by serendipity. Modern drug discovery is more focused and streamlined. It starts with target identification, followed by the identification of inhibitors that bind to the target and inhibit its activity. However, developing a new drug takes typically 10–12 years before it can be commercialized. Furthermore, drug discovery costs can range between several hundred million to billions of US dollars. Recent progresses in computational approaches have sped up drug discovery and development research. Computer-aided drug design (CADD) speeds up the hit-to-lead process and enables compounds to pass the barriers of preclinical testing in a short time. Molecular dynamics (MD) simulation has emerged as an important tool in the study of the conformational flexibility and dynamics of drug-target complexes. MD simulation helps to replicate the biological events in a computer simulation. It has become a routine computational tool for CADD and a revolution in the field of drug development. It provides an accurate estimate of thermodynamics and kinetics associated with drug-target interaction and binding. Development of new methods, software, and hardware has boosted the use of MD simulation among scientists working with CADD as well as in biopharmaceutical industry. Improvements in the force-field methods may further enhance the accuracy of free-energy predictions.

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References

  • Acharya, C., Coop, A., Polli, J. E., & Mackerell Jr., A. D. (2011). Recent advances in ligand-based drug design: Relevance and utility of the conformationally sampled pharmacophore approach. Current Computer-Aided Drug Design, 7(1), 10–22.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Amic, D., Davidovic-Amic, D., Beslo, D., Lucic, B., & Trinajstic, N. (1997). The use of the ordered Orthogonalized multivariate linear regression in a structure−activity study of Coumarin and flavonoid derivatives as inhibitors of aldose Reductase. Journal of Chemical Information and Computer Sciences, 37, 586.

    Article  Google Scholar 

  • Amic, D., Davidovic-Amic, D., Beslo, D., Lucic, B., & Trinajstic, N. (1998). QSAR of Flavylium salts as inhibitors of xanthine oxidase. Journal of Chemical Information and Computer Sciences, 38(5), 815–818.

    Article  CAS  Google Scholar 

  • Anderson, J. S., Mustafi, S. M., Hernandez, G., & LeMaster, D. M. (2014). Statistical allosteric coupling to the active site indole ring flip equilibria in the FK506-binding domain. Biophysical Chemistry, 192, 41–48.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Arkhipov, A., Shan, Y., Das, R., Endres, N. F., Eastwood, M. P., Wemmer, D. E., et al. (2013). Architecture and membrane interactions of the EGF receptor. Cell, 152(3), 557–569.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Bahar, I., & Rader, A. J. (2005). Coarse-grained normal mode analysis in structural biology. Current Opinion in Structural Biology, 15(5), 586–592.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Bender, A., & Glen, R. C. (2004). Molecular similarity: A key technique in molecular informatics. Organic & Biomolecular Chemistry, 2(22), 3204–3218.

    Article  CAS  Google Scholar 

  • Boczek, E. E., Reefschlager, L. G., Dehling, M., Struller, T. J., Hausler, E., Seidl, A., et al. (2015). Conformational processing of oncogenic v-Src kinase by the molecular chaperone Hsp90. Proceedings of the National Academy of Sciences of the United States of America, 112(25), E3189–E3198.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Bode, C., Kovacs, I. A., Szalay, M. S., Palotai, R., Korcsmaros, T., & Csermely, P. (2007). Network analysis of protein dynamics. FEBS Letters, 581(15), 2776–2782.

    Article  PubMed  CAS  Google Scholar 

  • Bonneau, R., & Baker, D. (2001). Ab initio protein structure prediction: Progress and prospects. Annual Review of Biophysics and Biomolecular Structure, 30, 173–189.

    Article  CAS  PubMed  Google Scholar 

  • Borrelli, K. W., Vitalis, A., Alcantara, R., & Guallar, V. (2005). PELE: Protein energy landscape exploration. A novel Monte Carlo based technique. Journal of Chemical Theory and Computation, 1(6), 1304–1311.

    Article  CAS  PubMed  Google Scholar 

  • Brela, M. Z., Wãjcik, M. J., Witek, J., Boczar, M., Wrona, E., Hashim, R., et al. (2016). Born-Oppenheimer molecular dynamics study on proton dynamics of strong hydrogen bonds in aspirin crystals, with emphasis on differences between two crystal forms. The Journal of Physical Chemistry. B, 120(16), 3854–3862.

    Article  CAS  PubMed  Google Scholar 

  • Chetri, P. B., Shukla, R., & Tripathi, T. (2019). Identification and characterization of glyceraldehyde 3-phosphate dehydrogenase from Fasciola gigantica. Parasitology Research, 118(3), 861–872.

    Article  PubMed  Google Scholar 

  • Daggett, V. (2000). Long timescale simulations. Current Opinion in Structural Biology, 10(2), 160–164.

    Article  CAS  PubMed  Google Scholar 

  • Damas, J. M., Filipe, L. C. S., Campos, S. R. R., Lousa, D., Victor, B. L., Baptista, A. M., et al. (2013). Predicting the thermodynamics and kinetics of Helix formation in a cyclic peptide model. Journal of Chemical Theory and Computation, 9(11), 5148–5157.

    Article  CAS  PubMed  Google Scholar 

  • Darden, T., Perera, L., Li, L., & Pedersen, L. (1999). New tricks for modelers from the crystallography toolkit: The particle mesh Ewald algorithm and its use in nucleic acid simulations. Structure, 7(3), R55–R60.

    Article  CAS  PubMed  Google Scholar 

  • Day, R., & Daggett, V. (2003). All-atom simulations of protein folding and unfolding. Advances in Protein Chemistry, 66, 373–403.

    Article  CAS  PubMed  Google Scholar 

  • Deganutti, G., Cuzzolin, A., Ciancetta, A., & Moro, S. (2015). Understanding allosteric interactions in G protein-coupled receptors using supervised molecular dynamics: A prototype study analysing the human A3 adenosine receptor positive allosteric modulator LUF6000. Bioorganic & Medicinal Chemistry, 23(14), 4065–4071.

    Article  CAS  Google Scholar 

  • Dorn, M., MB, E. S., Buriol, L. S., & Lamb, L. C. (2014). Three-dimensional protein structure prediction: Methods and computational strategies. Computational Biology and Chemistry, 53PB, 251–276.

    Article  PubMed  CAS  Google Scholar 

  • Durrant, J. D., & McCammon, J. A. (2011). Molecular dynamics simulations and drug discovery. BMC Biology, 9, 71.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Feig, M., Nawrocki, G., Yu, I., Wang, P., & Sugita, Y. (2018a). Challenges and opportunities in connecting simulations with experiments via molecular dynamics of cellular environments. Journal of Physics Conference Series, 1036, 012010.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Feig, M., Nawrocki, G., Yu, I., Wang, P., & Sugita, Y. (2018b). Challenges and opportunities in connecting simulations with experiments via molecular dynamics of cellular environments. Journal of Physics Conference Series, 1036, 012010.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Freddolino, P. L., Harrison, C. B., Liu, Y., & Schulten, K. (2010). Challenges in protein folding simulations: Timescale, representation, and analysis. Nature Physics, 6(10), 751–758.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Friesner, R. A., Banks, J. L., Murphy, R. B., Halgren, T. A., Klicic, J. J., Mainz, D. T., et al. (2004). Glide: A new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. Journal of Medicinal Chemistry, 47(7), 1739–1749.

    Article  CAS  PubMed  Google Scholar 

  • Ganesan, A., Coote, M. L., & Barakat, K. (2017). Molecular dynamics-driven drug discovery: Leaping forward with confidence. Drug Discovery Today, 22(2), 249–269. https://doi.org/10.1016/j.drudis.2016.11.001

    Article  CAS  PubMed  Google Scholar 

  • Genheden, S., & Ryde, U. (2015). The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opin Drug Discov, 10(5), 449–461.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Gervasio, F. L., Laio, A., & Parrinello, M. (2005). Flexible docking in solution using metadynamics. Journal of the American Chemical Society, 127(8), 2600–2607.

    Article  CAS  PubMed  Google Scholar 

  • Gigosos, M. A., Gonzãlez-Herrero, D., Lara, N., Florido, R., Calisti, A., Ferri, S., et al. (2018a). Classical molecular dynamics simulations of hydrogen plasmas and development of an analytical statistical model for computational validity assessment. Physical Review E, 98(3), 033307.

    Article  CAS  Google Scholar 

  • Gigosos, M. A., Gonzãlez-Herrero, D., Lara, N., Florido, R., Calisti, A., Ferri, S., et al. (2018b). Classical molecular dynamics simulations of hydrogen plasmas and development of an analytical statistical model for computational validity assessment. Physical Review E, 98(3), 033307.

    Article  CAS  Google Scholar 

  • Ginalski, K., Elofsson, A., Fischer, D., & Rychlewski, L. (2003). 3D-jury: A simple approach to improve protein structure predictions. Bioinformatics, 19(8), 1015–1018.

    Article  CAS  PubMed  Google Scholar 

  • Gupta, S., Shukla, H., Kumar, A., Shukla, R., Kumari, R., Tripathi, T., et al. (2020). Mycobacterium tuberculosis nucleoside diphosphate kinase shows interaction with putative ATP binding cassette (ABC) transporter, Rv1273c. Journal of Biomolecular Structure & Dynamics, 38(4), 1083–1093.

    Article  CAS  Google Scholar 

  • Hanson, S. M., Newstead, S., Swartz, K. J., & Sansom, M. S. P. (2015). Capsaicin interaction with TRPV1 channels in a lipid bilayer: Molecular dynamics simulation. Biophysical Journal, 108(6), 1425–1434.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Hawkins, P. C., & Nicholls, A. (2012). Conformer generation with OMEGA: Learning from the data set and the analysis of failures. Journal of Chemical Information and Modeling, 52(11), 2919–2936.

    Article  CAS  PubMed  Google Scholar 

  • Heidari, Z., Roe, D. R., Galindo-Murillo, R., Ghasemi, J. B., & Cheatham, T. E. (2016a). Using wavelet analysis to assist in identification of significant events in molecular dynamics simulations. Journal of Chemical Information and Modeling, 56(7), 1282–1291.

    Article  CAS  PubMed  Google Scholar 

  • Heidari, Z., Roe, D. R., Galindo-Murillo, R., Ghasemi, J. B., & Cheatham, T. E. (2016b). Using wavelet analysis to assist in identification of significant events in molecular dynamics simulations. Journal of Chemical Information and Modeling, 56(7), 1282–1291.

    Article  CAS  PubMed  Google Scholar 

  • Henzler-Wildman, K. A., Thai, V., Lei, M., Ott, M., Wolf-Watz, M., Fenn, T., et al. (2007). Intrinsic motions along an enzymatic reaction trajectory. Nature, 450(7171), 838–844.

    Article  CAS  PubMed  Google Scholar 

  • Hollingsworth, S. A., & Dror, R. O. (2018). Molecular dynamics simulation for all. Neuron, 99(6), 1129–1143.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Ishikawa, Y. (2013). A script for automated 3-dimentional structure generation and conformer search from 2- dimentional chemical drawing. Bioinformation, 9(19), 988–992.

    Article  PubMed  PubMed Central  Google Scholar 

  • Jo, S., Kim, T., Iyer, V. G., & Im, W. (2008). CHARMM-GUI: A web-based graphical user interface for CHARMM. Journal of Computational Chemistry, 29(11), 1859–1865.

    Article  CAS  PubMed  Google Scholar 

  • Joo, K., Lee, J., Lee, S., Seo, J.-H., Lee, S. J., & Lee, J. (2007). High accuracy template based modeling by global optimization. Proteins: Structure, Function, and Bioinformatics, 69, 83–89. https://doi.org/10.1002/prot.21628

    Article  CAS  Google Scholar 

  • Kalita, J., Shukla, R., Shukla, H., Gadhave, K., Giri, R., & Tripathi, T. (2017). Comprehensive analysis of the catalytic and structural properties of a mu-class glutathione s-transferase from Fasciola gigantica. Scientific Reports, 7(1), 17547.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Kalita, J., Shukla, R., & Tripathi, T. (2019). Structural basis of urea-induced unfolding of Fasciola gigantica glutathione S-transferase. Journal of Cellular Physiology, 234(4), 4491–4503.

    Article  CAS  PubMed  Google Scholar 

  • Kastner, J. (2011). Umbrella sampling. Wiley Interdisciplinary Reviews: Computational Molecular Science, 1, 932–942. https://doi.org/10.1002/wcms.66

    Article  CAS  Google Scholar 

  • Kim, M. K., Jernigan, R. L., & Chirikjian, G. S. (2002). Efficient generation of feasible pathways for protein conformational transitions. Biophysical Journal, 83(3), 1620–1630.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Kim, P. M., Lu, L. J., Xia, Y., & Gerstein, M. B. (2006). Relating three-dimensional structures to protein networks provides evolutionary insights. Science, 314(5807), 1938–1941.

    Article  CAS  PubMed  Google Scholar 

  • Klepeis, J. L., Lindorff-Larsen, K., Dror, R. O., & Shaw, D. E. (2009). Long-timescale molecular dynamics simulations of protein structure and function. Current Opinion in Structural Biology, 19(2), 120–127.

    Article  CAS  PubMed  Google Scholar 

  • Koshland Jr., D. E., Nemethy, G., & Filmer, D. (1966). Comparison of experimental binding data and theoretical models in proteins containing subunits. Biochemistry, 5(1), 365–385.

    Article  CAS  PubMed  Google Scholar 

  • Koshland, D. E. (1958). Application of a theory of enzyme specificity to protein synthesis. Proceedings of the National Academy of Sciences of the United States of America, 44(2), 98–104.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Kruger, P., Verheyden, S., Declerck, P. J., & Engelborghs, Y. (2001). Extending the capabilities of targeted molecular dynamics: Simulation of a large conformational transition in plasminogen activator inhibitor 1. Protein Science, 10(4), 798–808.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Kulczycka-Mierzejewska, K., Sadlej, J., & Trylska, J. (2018). Molecular dynamics simulations suggest why the A2058G mutation in 23S RNA results in bacterial resistance against clindamycin. Journal of Molecular Modeling, 24(8), 191.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Kumar, A., Grupcev, V., Berrada, M., Fogarty, J. C., Tu, Y. C., Zhu, X., et al. (2014). DCMS: A data analytics and management system for molecular simulation. Journal of Big Data, 2(1), 9. (2196-1115 (Print)).

    Article  PubMed  PubMed Central  Google Scholar 

  • Laio, A., & Gervasio, F. L. (2008). Metadynamics: A method to simulate rare events and reconstruct the free energy in biophysics, chemistry and material science. Reports on Progress in Physics, 71(12), 126601.

    Article  CAS  Google Scholar 

  • Lance, B. K., Deane, C. M., & Wood, G. R. (2010). Exploring the potential of template-based modelling. Bioinformatics, 26(15), 1849–1856.

    Article  CAS  PubMed  Google Scholar 

  • Lane, T. J., Shukla, D., Beauchamp, K. A., & Pande, V. S. (2013). To milliseconds and beyond: Challenges in the simulation of protein folding. Current Opinion in Structural Biology, 23(1), 58–65.

    Article  CAS  PubMed  Google Scholar 

  • Leherte, L., & Vercauteren, D. P. (2017). Reduced point charge models of proteins: Effect of protein-water interactions in molecular dynamics simulations of ubiquitin systems. The Journal of Physical Chemistry. B, 121(42), 9771–9784.

    Article  CAS  PubMed  Google Scholar 

  • Lindorff-Larsen, K., Piana, S., Dror, R. O., & Shaw, D. E. (2011). How fast-folding proteins fold. Science, 334(6055), 517–520.

    Article  CAS  PubMed  Google Scholar 

  • Linke, M., Kafinger, J., & Hummer, G. (2018). Rotational diffusion depends on box size in molecular dynamics simulations. Journal of Physical Chemistry Letters, 9(11), 2874–2878.

    Article  CAS  Google Scholar 

  • Loew, G. H., Villar, H. O., & Alkorta, I. (1993). Strategies for indirect computer-aided drug design. Pharmaceutical Research, 10(4), 475–486.

    Article  CAS  PubMed  Google Scholar 

  • Man, V. H., Nguyen, P. H., & Derreumaux, P. (2017). High-resolution structures of the amyloid-beta 1-42 dimers from the comparison of four atomistic force fields. The Journal of Physical Chemistry. B, 121(24), 5977–5987.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Marinari, E., & Parisi, G. (1992). Simulated tempering: A new Monte Carlo scheme. Europhysics Letters, 19(6), 451–458.

    Article  CAS  Google Scholar 

  • Martn-Garca, F., Papaleo, E., Gomez-Puertas, P., Boomsma, W., & Lindorff-Larsen, K. (2015). Comparing molecular dynamics force fields in the essential subspace. PLoS One, 10(3), e0121114.

    Article  CAS  Google Scholar 

  • Mason, J. S., Good, A. C., & Martin, E. J. (2001). 3-D pharmacophores in drug discovery. Current Pharmaceutical Design, 7(7), 567–597.

    Article  CAS  PubMed  Google Scholar 

  • McCammon, J. A., Gelin, B. R., & Karplus, M. (1977). Dynamics of folded proteins. Nature, 267(5612), 585–590.

    Article  CAS  PubMed  Google Scholar 

  • McHugh, S. M., Yu, H., Slough, D. P., & Lin, Y.-S. (2017). Mapping the sequence-structure relationships of simple cyclic hexapeptides. s, 19(4), 3315–3324.

    CAS  Google Scholar 

  • Meyer, T., D’Abramo, M., Hospital, A., Rueda, M., Ferrer-Costa, C., Pérez, A., et al. (2010). MoDEL (molecular dynamics extended library): A database of atomistic molecular dynamics trajectories. Structure, 18(11), 1399–1409. (1878-4186 (Electronic)).

    Article  CAS  PubMed  Google Scholar 

  • Misura, K. M., & Baker, D. (2005). Progress and challenges in high-resolution refinement of protein structure models. Proteins, 59(1), 15–29.

    Article  CAS  PubMed  Google Scholar 

  • Monod, J., Changeux, J. P., & Jacob, F. (1963). Allosteric proteins and cellular control systems. Journal of Molecular Biology, 6, 306–329.

    Article  CAS  PubMed  Google Scholar 

  • Monod, J., Wyman, J., & Changeux, J. P. (1965). On the nature of allosteric transitions: A plausible model. Journal of Molecular Biology, 12, 88–118.

    Article  CAS  PubMed  Google Scholar 

  • Moult, J. (2005). A decade of CASP: Progress, bottlenecks and prognosis in protein structure prediction. Current Opinion in Structural Biology, 15(3), 285–289.

    Article  CAS  PubMed  Google Scholar 

  • Nabuurs, S. B., Wagener, M., & de Vlieg, J. (2007). A flexible approach to induced fit docking. Journal of Medicinal Chemistry, 50(26), 6507–6518.

    Article  CAS  PubMed  Google Scholar 

  • Nguyen, P. H., Li, M. S., & Derreumaux, P. (2011). Effects of all-atom force fields on amyloid oligomerization: Replica exchange molecular dynamics simulations of the Abeta(16-22) dimer and trimer. Physical Chemistry Chemical Physics, 13(20), 9778–9788.

    Article  CAS  PubMed  Google Scholar 

  • Nguyen, P. H., Okamoto, Y., & Derreumaux, P. (2013). Communication: Simulated tempering with fast on-the-fly weight determination. The Journal of Chemical Physics, 138(6), 061102.

    Article  PubMed  CAS  Google Scholar 

  • Noble, D. (2003). Will genomics revolutionise pharmaceutical R&D? Trends in Biotechnology, 21(8), 333–337.

    Article  CAS  PubMed  Google Scholar 

  • Orellana, L., Rueda, M., Ferrer-Costa, C., Lopez-Blanco, J. R., Chaca, P., & Orozco, M. (2010). Approaching elastic network models to molecular dynamics flexibility. Journal of Chemical Theory and Computation, 6(9), 2910–2923.

    Article  CAS  PubMed  Google Scholar 

  • Pandey, T., Shukla, R., Shukla, H., Sonkar, A., Tripathi, T., & Singh, A. K. (2017). A combined biochemical and computational studies of the rho-class glutathione s-transferase sll1545 of Synechocystis PCC 6803. International Journal of Biological Macromolecules, 94(Pt A), 378–385.

    Article  CAS  PubMed  Google Scholar 

  • Perdih, A., Kotnik, M., Hodoscek, M., & Solmajer, T. (2007). Targeted molecular dynamics simulation studies of binding and conformational changes in E. coli MurD. First published., 68. https://doi.org/10.1002/prot.21374

  • Piana, S., Lindorff-Larsen, K., & Shaw, D. E. (2012). Protein folding kinetics and thermodynamics from atomistic simulation. Proceedings of the National Academy of Sciences of the United States of America, 109(44), 17845–17850.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Piana, S., Lindorff-Larsen, K., & Shaw, D. E. (2013). Atomic-level description of ubiquitin folding. Proceedings of the National Academy of Sciences of the United States of America, 110(15), 5915–5920.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Pronk, S., Pall, S., Schulz, R., Larsson, P., Bjelkmar, P., Apostolov, R., et al. (2013). GROMACS 4.5: A high-throughput and highly parallel open source molecular simulation toolkit. Bioinformatics, 29(7), 845–854.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Rajendran, V., Shukla, R., Shukla, H., & Tripathi, T. (2018). Structure-function studies of the asparaginyl-tRNA synthetase from Fasciola gigantica: Understanding the role of catalytic and non-catalytic domains. The Biochemical Journal, 475(21), 3377–3391.

    Article  CAS  PubMed  Google Scholar 

  • Raval, A., Piana, S., Eastwood, M. P., Dror, R. O., & Shaw, D. E. (2012). Refinement of protein structure homology models via long, all-atom molecular dynamics simulations. Proteins, 80(8), 2071–2079.

    Article  CAS  PubMed  Google Scholar 

  • Razavi, A. M., Wuest, W. M., & Voelz, V. A. (2014). Computational screening and selection of cyclic peptide hairpin Mimetics by molecular simulation and kinetic network models. Journal of Chemical Information and Modeling, 54(5), 1425–1432.

    Article  CAS  PubMed  Google Scholar 

  • Roy, A., Kucukural, A., & Zhang, Y. (2010). I-TASSER: A unified platform for automated protein structure and function prediction. Nature Protocols, 5(4), 725–738.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Sali, A., & Blundell, T. L. (1993). Comparative protein modelling by satisfaction of spatial restraints. Journal of Molecular Biology, 234(3), 779–815.

    Article  CAS  PubMed  Google Scholar 

  • Schames, J. R., Henchman, R. H., Siegel, J. S., Sotriffer, C. A., Ni, H., & McCammon, J. A. (2004). Discovery of a novel binding trench in HIV integrase. Journal of Medicinal Chemistry, 47(8), 1879–1881.

    Article  CAS  PubMed  Google Scholar 

  • Schlitter, J., Engels, M., Krãger, P., Jacoby, E., & Wollmer, A. (1993). Targeted molecular dynamics simulation of conformational change-application to the T ↔ R transition in insulin. Mol Simulat, 10(2–6), 291–308.

    Article  CAS  Google Scholar 

  • Sfriso, P., Emperador, A., Orellana, L., Hospital, A., GelpA, J. L., & Orozco, M. (2012). Finding conformational transition pathways from discrete molecular dynamics simulations. Journal of Chemical Theory and Computation, 8(11), 4707–4718.

    Article  CAS  PubMed  Google Scholar 

  • Sfriso, P., Hospital, A., Emperador, A., & Orozco, M. (2013). Exploration of conformational transition pathways from coarse-grained simulations. Bioinformatics, 29(16), 1980–1986.

    Article  CAS  PubMed  Google Scholar 

  • Shen, Y., Maupetit, J., Derreumaux, P., & Tuffery, P. (2014). Improved PEP-FOLD approach for peptide and Miniprotein structure prediction. Journal of Chemical Theory and Computation, 10(10), 4745–4758.

    Article  CAS  PubMed  Google Scholar 

  • Shukla, H., Shukla, R., Sonkar, A., Pandey, T., & Tripathi, T. (2017a). Distant Phe345 mutation compromises the stability and activity of Mycobacterium tuberculosis isocitrate lyase by modulating its structural flexibility. Scientific Reports, 7(1), 1058.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Shukla, H., Shukla, R., Sonkar, A., & Tripathi, T. (2017b). Alterations in conformational topology and interaction dynamics caused by L418A mutation leads to activity loss of Mycobacterium tuberculosis isocitrate lyase. Biochemical and Biophysical Research Communications, 490(2), 276–282.

    Article  CAS  PubMed  Google Scholar 

  • Shukla, R., Chetri, P. B., Sonkar, A., Pakharukova, M. Y., Mordvinov, V. A., & Tripathi, T. (2018a). Identification of novel natural inhibitors of Opisthorchis felineus cytochrome P450 using structure-based screening and molecular dynamic simulation. Journal of Biomolecular Structure & Dynamics, 36(13), 3541–3556.

    Article  CAS  Google Scholar 

  • Shukla, R., Shukla, H., Kalita, P., Sonkar, A., Pandey, T., Singh, D. B., et al. (2018b). Identification of potential inhibitors of Fasciola gigantica thioredoxin1: Computational screening, molecular dynamics simulation, and binding free energy studies. Journal of Biomolecular Structure & Dynamics, 36(8), 2147–2162.

    Article  CAS  Google Scholar 

  • Shukla, R., Shukla, H., Kalita, P., & Tripathi, T. (2018c). Structural insights into natural compounds as inhibitors of Fasciola gigantica thioredoxin glutathione reductase. Journal of Cellular Biochemistry, 119(4), 3067–3080.

    Article  CAS  PubMed  Google Scholar 

  • Shukla, R., Shukla, H., Sonkar, A., Pandey, T., & Tripathi, T. (2018d). Structure-based screening and molecular dynamics simulations offer novel natural compounds as potential inhibitors of Mycobacterium tuberculosis isocitrate lyase. Journal of Biomolecular Structure & Dynamics, 36(8), 2045–2057.

    Article  CAS  Google Scholar 

  • Shukla, R., Shukla, H., & Tripathi, T. (2018e). Activity loss by H46A mutation in Mycobacterium tuberculosis isocitrate lyase is due to decrease in structural plasticity and collective motions of the active site. Tuberculosis (Edinburgh, Scotland), 108, 143–150.

    Article  CAS  Google Scholar 

  • Shukla, R., & Tripathi, T. (2020). Molecular dynamics simulation of protein and protein-ligand complexes. In D. B. Singh (Ed.), Computer-Aided Drug Design (pp. 133–161). Singapore: Springer. https://doi.org/10.1007/978-981-15-6815-2_7. ISBN 978-981-15-6814-5.

    Chapter  Google Scholar 

  • Slough, D. P., Yu, H., McHugh, S. M., & Lin, Y. S. (2017). Toward accurately modeling N-methylated cyclic peptides. Physical Chemistry Chemical Physics, 19(7), 5377–5388.

    Article  CAS  PubMed  Google Scholar 

  • Sonkar, A., Shukla, H., Shukla, R., Kalita, J., & Tripathi, T. (2019). Unfolding of Acinetobacter baumannii MurA proceeds through a metastable intermediate: A combined spectroscopic and computational investigation. International Journal of Biological Macromolecules, 126, 941–951.

    Article  CAS  PubMed  Google Scholar 

  • Stroet, M., Caron, B., Visscher, K. M., Geerke, D. P., Malde, A. K., & Mark, A. E. (2018). Automated topology builder version 3.0: Prediction of solvation free enthalpies in water and hexane. Journal of Chemical Theory and Computation, 14(11), 5834–5845.

    Article  CAS  PubMed  Google Scholar 

  • Sugita, Y., & Okamoto, Y. (1999). Replica-exchange molecular dynamics method for protein folding. Chemical Physics Letters, 314(1), 141–151.

    Article  CAS  Google Scholar 

  • Talele, T. T., Khedkar, S. A., & Rigby, A. C. (2010). Successful applications of computer aided drug discovery: Moving drugs from concept to the clinic. Current Topics in Medicinal Chemistry, 10(1), 127–141.

    Article  CAS  PubMed  Google Scholar 

  • Thibault, J. C., Facelli, J. C., & Cheatham III, T. E. (2013). iBIOMES: Managing and sharing biomolecular simulation data in a distributed environment. Journal of Chemical Information and Modeling, 53(3), 726–736. (1549-960X (Electronic)).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Todd, M. H. (2005). Computer-aided organic synthesis. Chemical Society Reviews, 34(3), 247–266.

    Article  CAS  PubMed  Google Scholar 

  • van Aalten, D. M. F., Bywater, R., Findlay, J. B. C., Hendlich, M., Hooft, R. W. W., & Vriend, G. (1996). PRODRG, a program for generating molecular topologies and unique molecular descriptors from coordinates of small molecules. Journal of Computer-Aided Molecular Design, 10(3), 255–262.

    Article  PubMed  Google Scholar 

  • Verma, J., Khedkar, V. M., & Coutinho, E. C. (2010). 3D-QSAR in drug design--a review. Current Topics in Medicinal Chemistry, 10(1), 95–115.

    Article  CAS  PubMed  Google Scholar 

  • Verma, R. P., & Hansch, C. (2009). Camptothecins: A SAR/QSAR study. Chemical Reviews, 109(1), 213–235.

    Article  CAS  PubMed  Google Scholar 

  • Wang, Y., Shaikh, S. A., & Tajkhorshid, E. (2010). Exploring Transmembrane diffusion pathways with molecular dynamics. Physiology, 25(3), 142–154.

    Article  CAS  PubMed  Google Scholar 

  • Wouters, O. J., McKee, M., & Luyten, J. (2020). Estimated Research and Development investment needed to bring a new medicine to market, 2009-2018. JAMA, 323(9), 844–853. https://doi.org/10.1001/jama.2020.1166

    Article  PubMed  PubMed Central  Google Scholar 

  • Yang, S. Y. (2010). Pharmacophore modeling and applications in drug discovery: Challenges and recent advances. Drug Discovery Today, 15(11–12), 444–450.

    Article  CAS  PubMed  Google Scholar 

  • Zhang, C., & Ma, J. (2008). Comparison of sampling efficiency between simulated tempering and replica exchange. The Journal of Chemical Physics, 129(13), 134112.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Zhang, J., Xu, F., Hong, Y., Xiong, Q., & Pan, J. (2015a). A comprehensive review on the molecular dynamics simulation of the novel thermal properties of graphene. RSC Advances, 5(109), 89415–89426.

    Article  CAS  Google Scholar 

  • Zhang, J., Xu, F., Hong, Y., Xiong, Q., & Pan, J. (2015b). A comprehensive review on the molecular dynamics simulation of the novel thermal properties of graphene. RSC Advances, 5(109), 89415–89426.

    Article  CAS  Google Scholar 

  • Zhang, T., Nguyen, P. H., Nasica-Labouze, J., Mu, Y., & Derreumaux, P. (2015c). Folding atomistic proteins in explicit solvent using simulated tempering. The Journal of Physical Chemistry. B, 119(23), 6941–6951.

    Article  CAS  PubMed  Google Scholar 

  • Zhang, Y. (2009). Protein structure prediction: When is it useful? Current Opinion in Structural Biology, 19(2), 145–155.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Authors and Affiliations

  1. Molecular and Structural Biophysics Laboratory, Department of Biochemistry, North-Eastern Hill University, Shillong, India

    Rohit Shukla & Timir Tripathi

  2. Department of Biotechnology and Bioinformatics, Jaypee University of Information Technology (JUIT), Solan, Himachal Pradesh, India

    Rohit Shukla

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  1. Rohit Shukla
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  2. Timir Tripathi
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  1. Computer Aided Drug Design and Molecular Modelling Lab, Department of Bioinformatics, Alagappa University, Karaikudi, Tamil Nadu, India

    Sanjeev Kumar Singh

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Shukla, R., Tripathi, T. (2021). Molecular Dynamics Simulation in Drug Discovery: Opportunities and Challenges. In: Singh, S.K. (eds) Innovations and Implementations of Computer Aided Drug Discovery Strategies in Rational Drug Design. Springer, Singapore. https://doi.org/10.1007/978-981-15-8936-2_12

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